This project focuses on poor motor coordination, which is a ubiquitous finding in people with damage to the nervous system. Incoordination leads to poor trajectory and targeting control, and is most distinctly related to cerebellar dysfunction. The fundamental mechanism of cerebellar incoordination will be investigated by comparing human performance to computational models, and then by developing robotic control strategies that compensate for motor impairments. Compensation is essential for some individuals with cerebellar damage, since learning mechanisms can be inefficient or absent. An existing robotic exoskeleton device, the KinArm, will be used to acquire behavioral data during reaching tasks performed by control and cerebellar subjects, and dynamic models of the human arm will be used to determine the source of the differences between control and cerebellar data. Specifically, the dynamic models of control subjects will be used to simulate the effects of misestimation of limb dynamics. The dynamic parameters resulting in the best match will be used to inform a rational control strategy to help subjects achieve normal movement patterns. Then, a new, lightweight, safe, passive, take-home exoskeleton device will be designed and prototyped to enable implementation and testing of the optimal compensation techniques in short- and long-term studies. This project lays the foundation for novel home therapies by identifying, implementing, and testing strategies a robot could use to normalize movement control of people with cerebellar damage. The approach could eventually be extended to a broad range of patient populations, affecting the quality of life of millions of people with neurological disorders.